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首页> 外文期刊>The Journal of the Acoustical Society of America >Automatic estimation of position and orientation of an acoustic source by a microphone array network
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Automatic estimation of position and orientation of an acoustic source by a microphone array network

机译:通过麦克风阵列网络自动估计声源的位置和方向

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摘要

A method which automatically provides the position and orientation of a directional acoustic source in an enclosed environment is proposed. In this method, different combinations of the estimated parameters from the received signals and the microphone positions of each array are used as inputs to the artificial neural network (ANN). The estimated parameters are composed of time delay estimates (TDEs), source position estimates, distance estimates, and energy features. The outputs of the ANN are the source orientation (one out of four possible orientations shifted by 90° and either the best array which is defined as the nearest to the source) or the source position in two dimensional/three dimensional (2D/3D) space. This paper studies the position and orientation estimation performances of the ANN for different input/output combinations (and different numbers of hidden units). The best combination of parameters (TDEs and microphone positions) yields 21.8% reduction in the average position error compared to the following baselines and a correct orientation ratio greater than 99%. Position localization baselines consist of a time delay of arrival based method with an average position error of 34.1 cm and the steered response power with phase transform method with an average position error of 29.8 cm in 3D space.
机译:提出了一种在封闭环境中自动提供定向声源的位置和方向的方法。在这种方法中,将从接收到的信号的估计参数与每个阵列的麦克风位置的不同组合用作人工神经网络(ANN)的输入。估计的参数由时间延迟估计(TDE),源位置估计,距离估计和能量特征组成。 ANN的输出是源方向(偏移90°的四个可能方向中的一个,并且是定义为最接近源的最佳阵列)或源于二维/三维(2D / 3D)的位置空间。本文研究了不同输入/输出组合(以及不同数量的隐藏单元)的人工神经网络的位置和方向估计性能。与以下基准相比,参数(TDE和麦克风位置)的最佳组合可使平均位置误差降低21.8%,并且正确的定向比大于99%。位置定位基准线由基于时延的方法(平均位置误差为34.1 cm)和带相位转换方法的转向响应功率(在3D空间中的平均位置误差为29.8 cm)组成。

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